Decision Support Software for Optimal Decarbonisation Policymaking for Carbon Constrained Growth in Developing Countries – a Malaysian Case Study
- Type: Conference Presentation
- Conference Type: AIChE Annual Meeting
- Presentation Date: November 10, 2021
- Duration: 19 minutes
- Skill Level: Intermediate
- PDHs: 0.50
Carbon emissions pinch analysis (CEPA) was developed as a set of high-level graphical energy planning tools that can be used to systematically allocate different carbon-emitting energy sources to different energy sinks/demands to not exceed CO2 emission limits (Tan and Foo, 2007). These demands can include different sectors of the economies, industries, or geographical regions. The tools are based on the concepts of pinch analysis, developed for increasing energy efficiency of industrial plants by using thermodynamic principles to find energy targets that can be used to maximise heat recovery within a process (Linnhoff et al., 1982). Similarly, CEPA tools can be used to plot composite curves of the energy sources and sinks and their carbon intensity (emissions per unit energy) versus energy quantity to generate a carbon-energy diagram. This visualisation tool can then be used by decision-makers to assess the requirements for zero-carbon energy sources, carbon capture and storage (Lee et al., 2009) or any excess carbon-intensive energy generators that may need to be decommissioned. Recently, negative emissions technologies (NETs) have also been considered, which can be incorporated into the CEPA diagrams as a potential way to reduce the impact of the energy system (Nair et al., 2020).
These techniques are now firmly established and have been widely applied to different scenarios, including for energy planning in many countries, such as China (Li et al., 2016), Ireland (Crilly and Zhelev, 2010), India (Priya and Bandyopadhyay, 2013), and Nigeria (Salman et al., 2019). However, despite the ease of interpretation and use, CEPA suffers from several limitations:
- While the CEPA tools provide overall benchmarks for CO2 reduction, it is tedious to carry out if detailed planning with many options is needed.
- The incorporation of practical constraints, e.g. limitations of certain fuel sources for a sector, financial constraints, etc. are difficult to include.
- CEPA assumes static datasets at a single point in time. As energy demands and generators fluctuate with time, and technological innovations, uptake and installations also vary with time, this is a major limitation. In addition, uncertainty cannot be explicitly incorporated.
In this work, we present a new software tool that uses mathematical optimisation models to set realistic targets for detailed energy planning through extending CEPA concepts. Using mixed integer linear programming (MILP) optimisation models, we generate potential solutions subjected to various practical considerations, such as technology-readiness levels of potential technologies, uptake times, budget, etc. through formulating the problem as a multi-period planning problem. In this way, we can propose realistic targets and the optimal pathways to achieve these, subject to industry, sector, or nation-specific constraints, formulated by the users. Where CEPA can be used to provide âbest caseâ scenarios, the presented tools can be used to provide tailored solutions and test a wide-range of potential scenarios simultaneously.
We present our model formulations, open-source code and a set of case studies wherein different policies and technologies are considered to set realistic CO2 emission targets for the developing country, Malaysia. The representative case studies, informed through our collaboration with the Malaysian government agency, Malaysian Green Technology and Climate Change Centre, focus on three sectors which contribute over 80% of their carbon emissions: power generation, public transport and the manufacturing sector. We demonstrate potential pathways to achieving the 45 % INDC set out by Malaysiaâs Paris Agreement commitments, particularly showing the potential role of NETs and how policy shifts are required to meet these. We also show how the tools can be used to cascade decisions and pathways to the sector level, to allow for targeted policy strategies.
The long-term aims of the project are to produce a suite of decision-making tools for decarbonisation policymaking through the development of a strategic planning framework based on rigorous deterministic mathematical optimisation models. The framework is also formulated in a way to be extended to address planning under uncertainty in future. These tools are of vital importance to guiding governments and industry towards attaining the goals of the Paris Agreement, help in future planning and setting emissions targets, and ensure that strategies selected are optimal and evidence-based.
Crilly, D., Zhelev, T., 2010, Further emissions and energy targeting: an application of CO2 emissions pinch analysis to the Irish electricity generation sector. Clean Tech. Environ. Policy, 12, 177â189.
Lee, S.C., Ng, D.K.S., Foo, D.C.Y, Tan, R.R., 2009, Extended pinch targeting techniques for carbon-constrained energy sector planning. Appl. Energy, 86, 60â67.
Li, Z., Jia, X., Foo, D.C.Y., Tan, R.R., 2016, Minimizing carbon footprint using pinch analysis: the case of regional renewable electricity planning in China. Appl. Energy, 184, 1051â1062.
Linnhoff, B., Townsend, D.W., Boland, D., 1982, User guide on process integration for the efficient use of energy. IChemE, Rugby.
Nair, P.N.S.B., Tan, R.R., Foo, D.C.Y., 2020, Extended Graphical Approach for the Deployment of Negative Emission Technologies. Ind. Eng. Chem. Res., 59, 42, 18977â18990.
Priya, G.S.K, Bandyopadhyay, S., 2013, Emission constrained power system planning: a pinch analysis based study of Indian electricity sector. Clean Tech. Env. Pol., 15, 771â782.
Salman, B., Nomanbhay, S., Foo, D.C.Y., 2018, Carbon emissions pinch analysis (CEPA) for energy sector planning in Nigeria. Clean Tech. Env. Pol. 21, 93â108.
Tan, R. R. and Foo, D. C. Y. (2007). Pinch Analysis Approach to Carbon-Constrained Energy Sector Planning. Energy, 32(8), 1422-1429.
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|AIChE Undergraduate Student Members||Free|